Synopses & Reviews
Basic Statistics for the Behavioral Sciences demystifies and fully explains statistics without leaving out relevant topics or simply presenting formulas, in a format that is non-threatening and inviting to students. Gary Heiman has written a textbook--clearly, patiently, and with an occasional touch of humor--that teaches students not only how to compute an answer on demand, but also why they should perform the procedure or what their answer reveals about the data. Heiman has achieved five objectives in writing this text: to take a conceptual-intuitive approach, to present statistics within an understandable research context, to deal directly and positively with student weaknesses in mathematics, to introduce new terms and concepts in an integrated way, and to create a text that students will enjoy as well as learn from!
Synopsis
Fourth edition.
About the Author
Gary Heiman is a professor at Buffalo State College. He received his Ph.D. in cognitive psychology from Bowling Green State University. Praised by reviewers and adopters for his readable prose and effective pedagogical skills, he has written four books for Houghton Mifflin (now Cengage Learning): STATISTICS FOR THE BEHAVIORAL SCIENCES, RESEARCH METHODS IN PSYCHOLOGY, UNDERSTANDING RESEARCH METHODS AND STATISTICS, AND ESSENTIAL STATISTICS FOR THE BEHAVIORAL SCIENCES.
Table of Contents
Note: Each Chapter begins with Getting Started and ends with Putting It All Together, a Chapter Summary, Key Terms, Review Questions, and Application Questions. Chapters 3-15 include a Summary of Formulas. 1. Introduction to Statistics Why Is It Important to Learn Statistics (and How Do You Do That?) Review of Mathematics Used in Statistics 2. Statistics and the Research Process The Logic of Research Samples and Populations Applying Descriptive and Inferential Statistics Understanding Experiments and Correlational Studies The Characteristics of Scores Statistics in Published Research: Using Statistical Terms 3. Frequency Distributions and Percentiles New Statistical Notation Why Is It Important to Know about Frequency Distributions? Simple Frequency Distributions Types of Frequency Distributions Relative Frequency and the Normal Curve Computing Cumulative Frequency and Percentile Statistics in Published Research: APA Publication Rules A Word about Grouped Frequency Distributions 4. Measures of Central Tendency; The Mean, Median, and Mode New Statistical Notation Why Is It Important to Know about Central Tendency? What Is Central Tendency? The Mode The Median The Mean Transformations and the Mean Deviations around the Mean Describing the Population Mean Summarizing an Experiment Statistics in Published Research: Using the Mean 5. Measures of Variability: Range, Variance, and Standard Deviation New Statistical Notation Why Is It Important to Know about Measures of Variability? The Range Understanding the Variance and Standard Deviation Computing the Sample Variance and Sample Standard Deviation The Population Variance and the Population Standard Deviation Summary of the Variance and Standard Deviation Applying the Variance and Standard Deviation to Research Statistics in Published Research: Reporting Variability 6. z-Scores and the Normal Curve Model New Statistical Notation Why Is It Important to Know about z-Scores? Understanding z-Scores Interpreting z-Scores Using the z-Distribution Using z-Scores to Compare Different Variables Using z-Scores to Determine the Relative Frequency of Raw Scores Statistics in Published Research: Using z-Scores Using z-Scores to Describe Sample Means 7. The Correlation Coefficient New Statistical Notation Why Is It Important to Know about Correlation Coefficients? Understanding Correlational Research Types of Relationships Strength of the Relationship The Pearson Correlation Coefficient The Restriction of Range Problem Correlations in the Population Statistics in Published Research: Correlation Coefficients 8. Linear Regression New Statistical Notation Why Is It Important to Know about Linear Regression? Understanding Linear Regression The Linear Regression Equation Describing the Errors in Prediction Computing the Proportion of Variance Accounted For A Word About Multiple Correlation and Regression Statistics in Published Research: Linear Regression 9. Using Probability to Make Decisions about Data New Statistical Notation Why Is It Important to Know about Probability? The Logic of Probability Computing Probability Obtaining Probability from the Standard Normal Curve Random Sampling and Sampling Error Deciding Whether a Sample Represents a Population 10. Introduction to Hypothesis Testing New Statistical Notation Why Is It Important to Know about the z-Tests? The Role of Inferential Statistics in Research Setting Up Inferential Procedures Performing the z-Test Interpreting Significant Results Interpreting Nonsignificant Results Summary of the z-Test Statistics in Published Research: Reporting Significance Tests The One-Tailed Test Errors in Statistical Decision Making 11. Performing the One-Sample t-Test and Testing Correlation Coefficients New Statistical Notation Why Is It Important to Know about t-Tests? Performing the One-Sample t-Test Estimating ? by Computing a Confidence Interval Statistics in Published Research: Reporting the t-Test Significance Tests for Correlation Coefficients Maximizing the Power of Statistical Tests 12. The Two-Sample t-Test New Statistical Notation Why Is It Important to Know about the Two-Sample t-Test? Understanding the Two-Sample Experiment The Independent-Samples t-Test Summary of the Independent-Samples t-Test The Related-Samples t-Test Summary of the Related-Samples t-Test Describing the Relationship in a Two-Sample Experiment Statistics in Published Research: The Two-Sample Experiment 13. The One-Way Analysis of Variance New Statistical Notation Why Is It Important to Know about ANOVA? An Overview of ANOVA Components of the ANOVA Performing the ANOVA Performing Post Hoc Comparisons Summary of Steps in Performing a One-Way ANOVA Additional Procedures in the One-Way ANOVA Statistics in Published Research: Reporting ANOVA 14. The Two-Way Analysis of Variance New Statistical Notation Why Is It Important to Know about the Two-Way ANOVA? Understanding the Two-Way Design Overview of the Two-Way, Between-Subjects ANOVA Computing the Two-Way ANOVA Interpreting the Two-Way Experiment Summary of the Steps in Performing a Two-Way ANOVA Statistics in Published Research: Reporting ANOVA 15. Chi Square and Other Nonparametric Procedures New Statistical Notation Why Is It Important to Know about Nonparametric Procedures? Chi Square Procedures One-Way Chi Square The Two-Way Chi Square Statistics in Published Research: Reporting Chi Square Nonparametric Procedures for Ranked Data Appendices A. Additional Statistical Formulas B. Using SPSS C. Statistical Tables D. Answers to Odd-Numbered Questions